Category Archives: Defining the Future

It always helps to have a concrete example. I love learning by doing, so I’ve decided to deep dive into the process for one startup, see what I can learn from it, and share the insights I glean so we can all get a greater understanding of growth hacking. Join me.

growth hacking with code(love)

Now, where do we begin?

I like to say that startup growth should be an orchestra: different sections are trying different notes—when you listen to it from the crowd while they’re practicing, you can hear a consonance of some kind, but what’s happening is that each section is practicing how it should tune itself in sync with how the section sounds—and ultimately, how the orchestra itself sounds.

The beauty you hear at the end of a well-practiced orchestra comes from a series of players optimizing for their individual performance, as well as their collective one.

Brian Balfour, VP Growth of Hubspot, nailed it for me: he talks about how the most common question he gets is: what magic growth hack will propel your startup to instant success?

That’s a cop-out.

There’s no single magic bullet. The orchestra can’t just get a superstar violin player that can catapult the whole collective to success: they have to work so that each section of the orchestra gets progressively better, working within their individual units and the whole to deliver incredible, measured results.

There’s no magic bullet to startup growth.

You need a process that takes in new plans, works on existing ones, and gets new and existing players to work together to deliver magic, learning from yesterday’s mistakes to deliver progressively better results, forever experimenting and tinkering to do better.

For a system you need the aforementioned process, data, creative experiment ideas, and goals to aim for.

For a system you need the aforementioned process, data, creative experiment ideas, and goals to aim for.

I’ve worked with several startups already, but I’ve never had a complete taste of all of the data out there. This has been a function of my location as well as my inclinations: I’m based in Montreal, so I don’t have easy access to all of the startups clustered in major American cities. I’m also inclined towards earlier-stage startups where there isn’t as much data assembled: the startups are still going through the process of learning exactly how to grow. Most of them haven’t had the time to really look back at the data they’ve assembled.

You might know about Buffer from the great content they put out, not only on their blog, but through the social media management tool that forms the basis of their business. Buffer allows you to queue up a vast array of social media posts: the software handles the optimal timing for everything, allowing you to collect data on the effectiveness and reach of your social media efforts, while reducing the friction of organizing content for those efforts.

Playing with the data yields a whole host of insights. It’ll allow us to come up with experiment ideas, and goals that make sense to “growth hack” systematically, and push Buffer even further into the green.

“[Growth hacking is] experiment-driven marketing.” – Sean Ellis

Caveat here: I might make mistakes in reasoning or gathering numbers here: if I do, I want you to call me out on them in the comments productively. We’re all in here to learn together.

Also, none of this is conclusive because we don’t have all of the data.

It’s very difficult to parse cohort-by-cohort analysis from the open Buffer dashboard, one of the things you should do because each collection of users goes through a different life-cycle of using the product. This incomplete view on the data has a negative effect on any data analysis.

Buffer is a software-as-a-service business, where their users pay monthly for premium features, a typical freemium subscription model. Churn is the enemy of this model. The number of people who jump ship after becoming a subscriber decreases the amount of money you can count on coming in every month.

Most cancellations occur in the first month of a subscription. A churn rate increase, one of the red flags all startups are told to watch for, can be deceptive in this case. You won’t know if it is a function of your product going bad. It could mean that you’re experiencing explosive growth that is driving churn because a larger portion of your userbase are hitting that first month wall with higher cancellation rates. You wouldn’t know if this was the case unless you could measure growth cohort-by-cohort, and separate out old users unhappy with the product, and new ones are just trying it out.

We must also keep in mind that this is insight from Buffer’s Stripe account, which means we’re looking exclusively at when Buffer collects money: that’s fine, but we have no idea of, among other things, when Buffer spends money.

The metrics are also anonymized so we have no idea of who the clients are, which helps with privacy issues, but hurts when it comes to making tangible recommendations—beyond hypotheses, assumptions and some aggregated data, we can’t really target great clients based on little more than the revenue categories they’ve been placed in.

Nevertheless, the Buffer dashboard is a great look into one of the fastest growing consumer applications in the world: a set of numbers that implies a lot of activity, and interesting insights to be gained. Finding trends in the data is where all good growth hacking begins, so let’s start.

We have to look at all of the data before we find trends. The current situation looks strong, but there can be improvements. Monthly recurring revenue at $367,000 implying an annual run rate ($367,000 * 12) of $4.4 million (4.404 million to be anatomically precise) means that we’re looking at a business that has hit product-market fit. There are 28,484 current paying customers. Buffer long ago hit the 1 million total user mark: it happened in September 2013, more than a year ago.

In the last Buffer Open update, CEO Joel Gascoigne mentions that there were 1,725,172 total users of Buffer. Extrapolating the average of the two previous monthly growth rates in users (3.3% and 3.7% average to 3.5%)—I’d do a weighted average, but that’s just being pedantic at this point, then you can assume that at mid October, there are now 1,816,800 users assuming a growth rate of 3.5% that takes to us to the end of September, and 1.75% that takes us to the end of October.

Buffer with code(love)

We now have enough to get almost all of the metrics Kissmetrics considers must-have for SaaS businesses at this stage: monthly recurring revenue, churn, average revenue per user, and the lifetime value of customers. Acquisition cost, as mentioned above, is something we’re going to have to assume.

MRR (Monthly Recurring Revenue—the lifeline of a subscription business. How much money keeps on coming in month-by-month: $367,000, growing at a 5.7% clip in the last 30 days.

Joel mentions that August’s 4.8% growth rate was tepid and that he would like to focus on getting it up. The slow growth rate might have to do with the seasonality of the product, given that August is a slow month for social media professionals, an important point to always remember when treating numbers: they always have a greater context. July’s growth was 6.6%.

Getting monthly recurring revenue growth rate up would seem to be a key performance indicator, something important to move the chains on, and measure, because it represents a crucial factor to the health of any business. That’s something we’ll definitely have to consider on our experiment proposals: we’re going to focus more on tasks that are lower in the acquisition funnel, and which deliver money now.

Churn rate (the number of people who are unsubscribing from the service): Monthly user churn: 5.1%, decreasing 5.1% in the last 30 days.

Monthly revenue churn: 6.5%, increasing 2% in the last 30 days.

Seems like good news on the user front, but remember, August was a slower month than most. A churn rate decrease could just be less people hitting that first month wall. A year ago, monthly churn was around 4.4% for context within Buffer. The yearly change in monthly churn rate is 15.8%, a significant uptick.

For context outside of Buffer—70% of SaaS companies had annual churn under 10%. 5-7% annual churn rate is acceptable for pure B2B SaaS companies according to Lincoln Murphy. Again though, Buffer is a strange beast with massive consumer and business applications, more akin to Evernote than any of the SaaS companies incorporated in the survey in the number of people it attracts. Still, a 5.1% monthly churn rate on a purely annualized basis (without taking into account cohort thinking) comes out to 61.2% annual user churn—meaning on an oversimplified basis that Buffer is losing more users than it keeps in a year. It’s something to keep in mind, even if it doesn’t look that bad given Buffer’s expansive user base of millions, and available consumer growth engines: churn rate is pretty high. Getting users might not be as important as keeping them, especially given that it often costs 4 to 6 times more to acquire new users than just to keep existing ones.

How is revenue churn increasing, while user churn is decreasing? Turns out that this a function of Buffer’s split of users into casual and business users, a key insight that will come back again and again.

The vast majority of Buffer users are on the Pro plan, which costs $10/month, or $102/year. This allows you to connect 12 social accounts, and queue 200 posts: basically, if you’re a small business or a freelancer doing social media management, Buffer is reaching out to you. On the other end, there are business plans that can connect hundreds of accounts that will cost you upwards of $250/month.

It appears that monthly user churn of heavy enterprise accounts is really high, a function of the fact that there are so few, and that this seems to be a relatively new cohort. Monthly churn rates for some of these categories are as high as 33.3% (though that seems to me to be a function of the fact that there are so few of these plans). They’re such heavy money earners, even though user churn might be going down as a function of less Pro/Awesome plan members churning, revenue churn can still increase: an important point we’ll return to.

Average Revenue Per User: $13, growing at a 1.7% clip per month.

In a year, this has grown by about 26%, which is relatively static considering monthly recurring revenue has grown by 146% at the same clip. This seems to indicate that the majority of growth is coming from new users rather than finding ways to up-sell current users.

Given that the Awesome/Pro plan is really the only option for consumers, and the rest are billed as business plans, this makes a lot of sense. Upsells would only typically happen if people were transitioning from the Awesome plan to Business plans, suggesting a user behavior pattern of trying the Awesome plan to validate if it can work for a business.

The number of upgrades last year were 3,264, compared to 1,196 downgrades. However, this is somewhat confusing because Buffer counts switches from monthly to annual plans as a downgrade—despite the fact that annual plans actually have a much higher LTV than monthly ones (more on that later). In total, you can surmise that 4,460 people were moving around in the Buffer system last year. Considering that there were 28,424 customers, an oversimplified metric that didn’t account for users doing multiple transitions, and the changing composition of Buffer customers, would indicate that about 15.7% of people move from one tier to another, after only about 1.5% go to being paying customers in the first place. What this means is that people are ready to be upsold, but perhaps Buffer hasn’t captured as much value from that as it should.

Average LTV (Lifetime Value, or how much Buffer gets on average from each client in their full stay with Buffer) of customer: $251, up 7.2% from the last 30 days

This is where Ivan’s analysis of Buffer shines. You realize that there are large LTV values for Enterprise clients but those on the Awesome plan pale in comparison: this is a quantity over quality play, but the contrast is quite stark: $583 LTV for the yearly Pro plan, and merely $134 for the monthly one. This in comparison to the enterprise plans, which often average many times more—some above $25,000 in LTV. You can make a theory that finding ways to commit people to the annual plan will drastically increase MRR, by reducing revenue and user churn.

Given some insight I reached by meeting with Buffer’s COO, Leo, I’ve realized that these are soft-sell clients who have passed the limits of standard plans and asked for custom limits. Given how high the LTV is however, it may well be worth the time to find out how to formalize this arrangement.

My meeting with Leo, COO of Buffer

I managed to snag a meeting with the COO of Buffer, who gave me a lot of insights. I’ve realized that Buffer’s analytics and data are the biggest sell for enterprise clients, that Buffer doesn’t use paid acquisition (doesn’t really work for them) and the biggest growth engines are built into the product itself.

Important insights from the data

Enterprise clients are really important—and it seems Buffer is moving to that direction.

Buffer’s profile is akin to Evernote’s: freemium with a high customer base, and higher adoption rates, but with higher churn rates as a result. Churn can be lowered but it’s not the biggest issue here.

Buffer focuses on its’ content and the product itself as its’ main avenues for growth: and its’ largely working.

Awareness isn’t as much of a problem as activation. The KPI Buffer is really looking for is increasing the monthly recurring revenue.

Buffer is affected by seasonality—it does rise and fall with the whims of its’ power base.

Buffer is seeing a lot of movement between tiers, but maybe not taking full advantage of it.

Experiment ideas

1-Get a contest going: best social media agency, or social media agency with the most clicks gets a free custom enterprise package from Buffer.

This will help spread more awareness of the enterprise options available, and business plans, and will bring out Buffer power users to compete against one another. If you mandate that they have to share their results with the Buffer team, and their tactics, highlighting the agency at the same time—you create a rich slew of content from heavy social media users about some of their tips and tricks.

This will create more awareness about business plans, opening up a new slew of rich, interactive content. Test to see if it is driving more adoption of business plans by linking contest content with sign ups, and tracking to see if this cohort of business sign ups have similar, better or worse metrics than the current average.

2-Create a bigger incentive to sign up for yearly plans.

Add prompts in the upgrade panel about an added benefit—A/B split test to see what works. It could be more posts in the queue,or more social profiles.Offer people who do two months straight an upsell to the yearly plan with an even better discount for being dedicated Buffer members. “Hey, we chose you because you’ve become a Buffer power user, get 20% off our yearly plan!”. Really try to lock people into it.

This should decrease churn, and increase monthly recurring revenue.

3-Post sample case studies of business Buffer analysis and have a Buffer content crafter go over it. Do and don’t of social media. This will highlight the most popular feature of Buffer for Business, and create content that highlights how powerful Buffer’s analytics dashboard truly is.

This should increase activation via signups from the analytics dashboard, and increase awareness of the Buffer for Business rollout.

4-Test a new pricing tier/test pricing

You don’t want to take away from the transition to the business plan, but perhaps a plan that fit in between the Awesome plan and the business plan would work at a flat yearly rate of $200/year. Create a pricing page where you preview a new tier and see how many people sign up, and if people downgrade from a business plan. If the pricing tier makes more MRR, keep it going.

So there you have it: data. A system to manage that data, use it to glean experiments, and measure their effects. If they move the needle, keep the experiments going and scale them. If they don’t, or work against metrics, scrap them.

I hoped you enjoyed this rundown of what growth hacking represents to me. If you want to challenge me on any of my assumptions, or thoughts, please comment below! I know that I don’t know many things, and I am constantly discovering new ways of learning. Please connect with me at [email protected] or at https://twitter.com/rogerh1991 if you want to further the discussion!

The following is an excerpt from my latest book, Entrepreneur Blackjack: 21 Startup Buzzwords Defined. The book is a guide to surviving startup cocktails, and building incredible new ventures. It defines a series of common startup buzzwords through storytelling and concrete examples so that everybody can learn from them, rather than using them to confuse.

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The minimum viable product is an art, and not a science.

It’s when you finally decide your product is good enough to test out your original experiment. It is the bare minimum needed to get data about the market you’re in, and how customers will actually react to your idea.

A good MVP is a vehicle for your hopes and dreams.

As with any first car, there are a few rough patches to deal with.

When Twitter first started, they were not the Twitter you know and love. Twitter revolved around users texting messages with their phones,posting those messages on a publicly accessible web platform.

Netflix’s first rough version wasn’t even delivering movies online: it was about delivering movies through mail.

It’s important to realize that way back when they were getting started, those companies didn’t command the billions of dollars they do now. Their resources had to be focused on one narrow test, and that test had to be delivered under the simplest conditions possible.

Twitter was testing whether or not people wanted to be part of an organized universal message board, somewhere where you could post your thoughts simply, and see them displayed for all to see. The largest unorganized cocktail party on Earth ensued. It was only afterwards that Twitter worked on refining the experience. The Twitter you know now that allows people to post directly on the platform through mobile applications, and allows them to easily find out what is happening around them through hashtags, and curated accounts—that was a Twitter that took years to evolve.

Netflix was trying to test a simple theory: would people be willing to pay for convenient access to movies? Instead of going to Blockbuster or anywhere else, would they prefer getting it shipped to their homes? If they did prefer that, it naturally followed that they would like to have video streamed to them on-demand with the web. After all, instead of dealing with the messiness of physical tapes, imagine instead a service that could deliver to you the media you wanted whenever you wanted it.

Netflix certainly imagined it. They executed on it masterfully.

Startup Timeline

The test for instant on-demand online access to media was validated. Consumers accepted it en masse. Along the way, Netflix learned a great deal about the pain they were solving, and how they could go about delivering video to their consumers in the best way.

Netflix decided to move to external servers after an internal data center failure almost wiped out their ability to deliver videos on the web—which at the time was an experimental feature. By moving over to a more secure solution, Netflix ensured that the experimental feature that would become the centerpiece of its business would always be reliable.

The minimum viable product allowed for these large giants to test ideas cheaply, and make sure that when the time was right to spread their product like wildfire, everything was on a solid foundation.

It is through the minimum viable product that you first collect the data you need to establish if you have an idea worth pursuing. It is here where you determine whether or not you might need to pivot. It is here that your idea becomes something tangible that can be shared throughout the web, rather than a figment of your imagination. It is here that you see whether or not you are solving a real problem—and whether or not you have real customers who need your idea to exist.

Pieter (aka Levels.io) is an entrepreneur who has committed to doing 12 startups in 12 months. He’s constructed a series of minimum viable products that have been featured on Wired, the Next Web, and a whole host of publications, and been used by hundreds of thousands of people.

His latest venture, NomadList, focuses on sorting the cities of the world so that you can distinguish how friendly they would be to remote workers, from climate conditions all the way to how LGBT-friendly the cities were.

Pieter is the perfect example of somebody who gets what a MVP should be about. When he talks about his ideas, he talks about building them as simply as possible, and just getting them done.

He started NomadList, a venture that was profitable from day 1, through creating a shared Google Spreadsheet, nothing more. He didn’t need to create a website or anything fancy to test the theory that people needed a guide to help them figure out cities where they might want to work remotely.

It can take you or me, or anybody less than five minutes to set up a Google Spreadsheet.

Soon after Pieter shared it, the list was getting populated with new information and new categories. Somebody filled in how LGBT-friendly each city was, rounding out the NomadList to its’ final form.

Demand for Pieter’s idea was established in the simplest fashion possible.

Perfect is the enemy of done. Done is when you can begin testing your theory that your idea is something people will use.

I don’t mean to do it—it’s just that startup buzzwords are so comfortable. Their familiar confines help mark me as being part of a very exclusive set of knowledge holders. They elevate me and put others down. They include those I want to talk with, and exclude those I don’t. A small part of me hates what I just said. A large part of me does it anyways.

To atone for my sins, I’ve decided to create a startup dictionary. No bullshit: a simple definition of the term, and an example. Next time you have to listen to me or anybody else who talks in buzzwords, you’ll at least be able to understand what we are talking about: and you should be part of the conversation.

If there’s anything I’m missing, please comment below.

Startup Dictionary with code(love)

Let’s start from the beginning:

The No-Bullshit Startup Dictionary: A

A/B Split Test: A random experiment where you test two variants of something against each other and see the results. Most often used to test variant A of a webpage and variant B to see which performs best, and gets more views and clicks. Example: See this case study by Optimizely.

Agile: Agile refers to agile software development. This means that instead of spending many years developing a web platform, many startups now release web platforms in a short time, then use live customer feedback to develop successive improvements on the first version. Example: See this test first, develop later mentality in action.

AirBnB: A web platform that allows you to rent out somebody’s guest room for a night or two, and to loan out your spare space as well. Typically seen in explanations like “My company is the AirBnB for 3D Printing”. Example: Check out their website.

Alibaba: A web platform that is used as a trading platform for wholesalers around the world. Most often brought up because they are about to go public and raise lots of money. Example: Check out their website.

Ajax: Ajax is a set of techniques that allow a webpage to reload data from the server without you having to reload the page itself. Example: Gmail was one of the pioneers of this.

Angel: An angel investor is often one of the very first people to provide funding for a startup in return for shares in your startup. Example: Paul Buchheit is the creator of Gmail, and has active angel investments in about 40 startups.

AngelList: A web platform where angel investors connect with startup founders. Example: Check it out here.

Annual Recurring Revenue: Typically applied because a lot of startups work on a monthly subscription basis, annual recurring revenue is a prediction of how much revenue is locked in with subscribers that will pay every year. Example: How Aaron Levie, CEO of Box, views Annual Recurring Revenue for his subscription based business.

API: An application programming interface is a set of standards for how software should communicate with one another. Web APIs allow for the easy transfer of data from one platform to another. Example: Twitter’s API allows you to search through tweets.

Asynchronous I/O: A form of input/output in technology that allows for many processes to happen at the same time, rather than going through one process at a time. Can often allow webpages to load faster. Example: The modern web software platform Node.JS built on Javascript is based on this technology and concept, allowing for real-time applications to get new data without reloading.

AWS: Amazon Web Services is a popular hosting solution for many startups. They host their websites on servers owned by AWS, which charges a fee for the service. Example: Check out their website.

This is a short excerpt of Build, a book on several extraordinary entrepreneurs and technologists building the future, and what they’ve learned doing so.

Sometimes, for a given problem, there are only a handful of people who have exactly the right advice. In a networked world where information can be spread effortlessly, the problem isn’t access to information: it is the filter to what solves your problem, and what doesn’t that really matters.

Dan Martell of Clarity.FM aims to be that filter. His platform allows you to instantly access the individual you need who has lived through your problems before you. Imagine a real-time Jeopardy call-your-friend function, if your friends were million-dollar entrepreneurs, and billion-dollar investors.

Welcome to the future of advice.

It looks a lot like a curated index of experts that are ready to answer your questions for a nominal fee, on-demand—and that’s because that’s what it has to be. Nowadays, to collect opinions is almost trivial. Posting a link anywhere is an open invitation for many on the web to openly question your sanity, and plenty more.

What most people need now is not information—there is too much of that around. What they need is qualified advice: communication that has more barriers than Yahoo Answers, in other words. People will spend immense amounts of time and effort to find those qualified answers, because quality matters more than quantity.

This can be seen in the success of Quora—which has managed to gather a community of very intelligent and connected contributors, who more often than not, know exactly what it is like to work with Elon Musk or Sergey Brin—because they are working with them right now.

The future of advice-giving to Dan isn’t about making it easier for advisor and advisee to connect with one another: it’s about creating a level of friction so that both sides know exactly what they’re getting into, and both sides know exactly how much they’d be willing to give up to meet one another.

The future of advice-giving isn’t about making it easier for advisor and advisee to connect with one another: it’s about creating a level of friction so that both sides know exactly what they’re getting into, and both sides know exactly how much they’d be willing to give up to meet one another.

That bike bought years ago, now stashed in the garage. The lawn mower we use once a week. The empty guest room left unoccupied.

It used to be that the economic value of these assets were quite low: after all, in order to rent out that empty guest room, you’d probably have to go out and make several phone calls, and pay for several advertisements.

That is no longer the case. One of the most powerful facets of the Internet is its ability to connect an infinite number of needs and wants frictionlessly, bringing together people who would have never had met.

One of the most powerful facets of the Internet is its ability to connect an infinite number of needs and wants with each other frictionlessly, bringing together people who would have never had met.

By providing targeted conduits for these channels of communication, you and I can find the person with the empty guest room we want to sleep in, and they can find us. We can communicate our mutual desires almost effortlessly. This changes the dynamics of our individual economics: now everything we have stashed away steadily increases in economic value. Wasted value becomes much harder to ignore.

AirBNB allows us to rent out what were once unused guest rooms. Outpost Travel allows us to search for rideshares, occupying otherwise unused car seats. Breather allows us to rent out what would be unused office space.

Welcome to the sharing economy.

New technologies allow us to communicate and coordinate amongst ourselves, unlocking potential that had once been stowed away. None of us can afford to miss out on this liberation of what had once been passive and wasted resources.

As the sharing economy matures, and clashes with old laws, issues will arise as new ideas will bump against old realities. Yet the sharing economy will remain. It defines the spirit of potential that embodies new technologies.

Brian Chesky, co-founder of AirBnB on old realities. From Valleywag

Back in the 1990s, Microsoft presented you with software top-down, created by a set of programmers that were assembled from the top-down. Now, you can get your software for free from a set of programmers that organically collaborated from the bottom-up to build a project they all believe in.

It is this spirit that resides in the sharing economy, a bottom-up approach to economic value that uses technology to connect people with mutually beneficial needs, allowing them to organically collaborate, unlocking what had previously been wasted value.

The sharing economy is a bottom-up approach to economic value that uses technology to connect people with mutually beneficial needs, allowing them to organically collaborate, unlocking what had previously been wasted value.

The Internet of Things is a new innovation that is sweeping into gradual mainstream awareness, if not adoption. It’s become a recent topic of some fascination, especially for Google-watchers who are trying to uncover the latest technological trends by following the Internet giant: surely the $3 billion dollar plus purchase of smart home device maker Nest did not escape notice.

The first thing Jeff notes is that what we conceptualize as the Internet of Things can be very exactly defined. Devices that communicate with one another have always existed. Harken back to your childhood when you used a remote control to control a toy car: would that not qualify as being part of the Internet of Things?

Jeff says no. The reason why is because the Internet of Things encompasses internet-enabled devices that can communicate with one another, with one very distinct defining trait: they can do so without any direct human input. As your toy car zips around, you are controlling it directly. However, a Nest thermostat can adjust the heat without you ever touching anything.

This is the magic of the Internet of Things. Jeff imagines a world of “smart spaces” where entire houses, and even neighbourhoods could shift to be adapted to you. A house could be heated at the right temperature, with the lights dimmed for the right ambiance, without you ever doing anything but the initial setup.

Smart Spaces with code(love)

Jeff’s company works on allowing for devices to identify you. reelyActive uses hardware RFID devices to tag you as you move through multiple spaces, therefore allowing for the possibility of “smart spaces” to grow, sooner than later. Already, Jeff is working on realizing a Google Analytics for retail at a low enough cost and without significant friction, perfectly suited for smaller retailers—this was a pipe dream just a few years ago. The world he imagines is coming sooner than later, and it can be summed up in one line.

The Internet of Things is a network of internet-enabled devices that can communicate with each other without direct human input, allowing for the evolution of smart spaces that can adapt to you without you doing anything at all.

The Internet of Things is a network of internet-enabled devices that can communicate with each other, without direct human input, allowing for the evolution of smart spaces that can adapt to you without you doing anything at all.

Growth hacking is a buzzword. As soon as somebody says it, the fury of meaning nothing, but signifying everything envelopes any situation you place it in. It’s mysterious and ambiguous, but it really doesn’t have to be.

It’s always been hard for me to figure out this term, and yet it’s been a necessity because I’ve always wanted to work in the field. I think part of what compelled me to get into building and scaling web platforms was the mystery of understanding what growth hacking was about: even the mysterious bits I could get out of it sounded cool. Some sort of marketing meets technology was something I thought would be ideal for me.

So, what I did was take a target list of everywhere I thought growth hackers might be, from mentoring sites, to tech entrepreneur networking sites—most notably FounderDating—to good old LinkedIn. I get familiar with the big names in the field. I reached out to many of them systematically, seeking the same insights: what exactly is growth hacking, how do you go about growth hacking, and how can I go about growth hacking? I then recorded the answers, and compared them, looking for some sort of pattern or formula that defined the concept.

In doing so, I realized that what I was doing embodied what growth hacking was all about. Trying out new stuff, and then measuring whether or not it was more efficient than what I had been doing before is the core of growth hacking. Every one of the answers pointed me to a direction, a direction that I can sum up in one line.

Growth hacking is being creative and trying new stuff, anything, to try to acquire new website users, measuring the effects of each individual outreach on the numbers of new engaged users, then determining whether it’s more efficient than what you were doing before on a monetary and time basis, and if so, piling as many of your resources as you can into those new channels.

To summarize even further: To growth hack, try new stuff, and measure whether or not you’re being more efficient driving users to your webpage in doing that new stuff.

To growth hack, try new stuff, and measure whether or not you’re being more efficient driving users in doing that new stuff.

Next time someone brings up growth hacking, make sure you share, and send them here. I’m measuring whether that works.

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Want a great resource to getting you started with the kind of actionable measures you can test and measure with growth hacking? Check out this list of 21 Actionable Growth Hacking Tactics.

I remember the first time I said the word “big data” with pride when describing my work. It, like every good buzzword, meant nothing to me, but conveyed a lot to my imagined prospective audience. It said something about my intelligence that I was working in “big data”, plying away at Excel sheets with way too many lines—a sure sign of a “big data” expert!

I know better now. After doing some research, I’m proud to say that I knew absolutely nothing about the topic at the time. In many ways, I still don’t—but I know enough to talk about the basics of “big data” and what it really represents, so you can explore with me.

The first step is to realize that big data represents data that is so large and complex that conventional data tools such as the table-based SQL cannot handle the load. Big data is not simply a big dataset that can be handled with Excel. Think of, for example, someone tracking every time someone commented on Ahnold’s accent on social media, their location, and other user attributes, in a mad quest to find who had the best “get to the choppa!” or “there is no bathroom!” quote variations: you’d quickly go mad trying to pass through every single one of those data points in a relational table or in an Excel file, even if you worked for a large Arnold-watching company, and had a set data process.

An easy rule of thumb to describe this is to say that big data refers to data sets that become difficult for an organization with a conventional data process to handle. This can be on several orders of magnitude. A smaller business may struggle with a lower threshold than a larger one. Nevertheless, it is the beginning of the struggle, and the search for alternatives to bread-and-butter SQL/Excel that is at the core of big data.

Traditional data tends to group data into tables, and operates with a smaller number of servers. Big data tends to ungroup data, and organize and analyze data through parallel processing across a larger number of servers.

When people in the field comment about the possibilities offered by big data, they are espousing the collection of unfathomable amounts of details we are now leaving on the web which was impossible five or ten years ago—because there were not so many details on the web, and there were no tools to collect them. Now with smartphones, sensors, and social media, data points are multiplying on an exponential level. Those who would take a dragnet over all of this data, pry them through tools not traditionally used in data collection that spread the volume and velocity of data over several servers instead of one or two, and then emerge with finely combed and actionable insights despite the overbearingly massive amount of data, are dealing with big data. This includes the NSA, but also data scientists who won the 2012 election, and health analysts working to ensure better care for all.